Grantee Research Project Results
2003 Progress Report: Not All Deaths are Created Equal: Understanding Individual Preferences for Reductions in Morbidity-Mortality Events
EPA Grant Number: R829485Title: Not All Deaths are Created Equal: Understanding Individual Preferences for Reductions in Morbidity-Mortality Events
Investigators: DeShazo, J. R. , Cameron, Trudy
Institution: University of California - Los Angeles , University of Oregon
Current Institution: University of California - Los Angeles
EPA Project Officer: Hahn, Intaek
Project Period: October 1, 2001 through September 30, 2003 (Extended to May 30, 2006)
Project Period Covered by this Report: October 1, 2002 through September 30, 2003
Project Amount: $360,756
RFA: Decision-Making and Valuation for Environmental Policy (2001) RFA Text | Recipients Lists
Research Category: Environmental Justice
Objective:
Much of the literature on deriving the Value of a Statistical Life (VSL) focuses on deriving a single one-size-fits-all VSL measure, a construct that measures the marginal rate of substitution between mortality risk and income. It is common to estimate wage-risk (or wealth-risk) trade-offs by assuming the individual considers a single health risk that is reduced with certainty in the current period. Our research generalizes the traditional single-risk, single-period VSL model in several ways.
After receiving supplemental funding from the U.S. Environmental Protection Agency, we have added three additional objectives to our original three objectives. Therefore, we pursue six major objectives in our effort to generalize the traditional VSL model, all of which involve accommodating various sources of heterogeneity when deriving individuals’ demands for risk reductions. They objectives of this research project are to: (1) develop a more structural model of demand that yields the present discounted value of private risk-reducing programs that yield future reductions in the risk of morbidity and premature mortality; (2) evaluate how the age of the respondent and the age at which risks are reduced affect current period demand for private risk-reducing programs; (3) evaluate how actual and expected illnesses affect individual’s demand for particular private risk-reduction programs; (4) develop a structural model of demand for public prevention programs that reduce risk in a variety of risk exposure contexts and a variety of health risk outcomes.; (5) develop a similar model of demand for treatment programs that reduce individuals’ risk of remaining ill or dying once they have become ill; and (6) compare demand for ex ante prevention programs versus ex post treatment programs that reduce the risk of various states of illness and premature death.
We also have the corollary objectives of designing and administering three surveys that collect data sets that permit us to empirically estimate individuals’ demand for: (1) private health risk mitigation programs; (2) public risk prevention programs; and (3) public treatment programs that reduce risk.
Progress Summary:
We have completed three of our major objectives and are endeavoring now to publish our findings. We also have designed and implemented our three surveys. We have produced draft papers for the first four objectives. We have papers that: (1) develop a more general model; (2) evaluate the role of age and latency on demand; and (3) evaluate the effects of actual and expected morbidity on demand. We have begun to disseminate our results.
Surveys and Collected Data
We have designed and implemented three surveys.
Survey of Demand for Private Risk Reductions Programs
We conducted a national survey of 2,439 U.S. respondents focusing on their demand for private risk reduction programs. We present respondents with an illness-specific health-risk reduction program that involves diagnostic screening, remedial medications, and lifestyle changes that would reduce their probability of experiencing that illness profile. Individuals must pay an annual fee to participate in each risk-reducing program. They are asked to choose between two risk-reducing programs (each associated with a different illness profile) or to reject both programs. An advantage of this choice setting is that the individual faces a portfolio of health risks that resemble those they actually face. Through their choices, individuals reveal trade-offs across specific illnesses and a full continuum of health states of different durations. We also observe them strategically allocating expenditures for risk mitigating programs across the current year and future years of their remaining life. Each health risk in our study is presented as an illness profile that describes a probabilistic time pattern of health states that the individual could experience. Each health profile consists of randomly assigned values for the individual's future age at the time of onset, the severity and duration of treatments and morbidity, the age at recovery (if there is any), and the number of lost life-years (if there are any).
Survey of Demand Public Risk Prevention Survey
To a nationally represented sample of approximately 1,600 individuals, we administered a stated preference survey for public risk prevention programs. Within a pair-wise choice set of prevention policies, we vary the number of illnesses prevented, the number of deaths avoided, the length of time the policy is in effect, the source of the health threat, the type of disease avoided, the size of the affected population, and several other attributes. We directly evaluate shifts in demand for categorically different types of risk exposures such as air, water, and food contaminants, as well as the risk of highway mortality and morbidity. We also evaluate a comprehensive set of illnesses and targeted groups, including cancer, leukemia, leukemia in children, colon/bladder cancer, asthma, asthma in children, lung cancer, heart disease, heart attack, stroke, respiratory disease, and motor vehicle accidents. Finally, we also elicited individual-specific measures of the incidence of the private benefits of each program as well as several attitudinal measures.
Survey of Demand for Public Mitigation of the Ex post Effects of Health Risk
Finally, again to a nationally representative sample of approximately 1,600 individuals, we administered a stated preference survey to elicit demand for publicly available treatment programs that reduce their risk of experiencing time spent in states of illness or of premature death. These interventions would increase the development and adoption of new types of medical treatments for a wide range of illnesses. The availability of these interventions would reduce the risk of death and increase the probability of recovery for those people who already are sick. In addition, treatment-interventions also enable us to examine how respondents demand changes when the treatment benefits specific groups within society. Specifically, we can evaluate how respondent demand shifts for interventions that benefit only children (asthma and leukemia), only adult, only seniors, or any combination of these groups. We also consider treatments that benefit only men (prostate cancer) and only women (breast cancer).
Paper in Progress for Publication
We have produced draft papers for the first four objectives. We have papers that: (1) develop a more general model; (2) evaluate the role of age and latency on demand; (3) evaluate the effects of actual and expected morbidity on demand; and (4) estimate the demand for public risk prevention programs.
We develop a utility-theoretic choice model in which individuals choose among alternative programs to reduce their risk of experiencing future years of illness and/or lost life-years. Unlike previous stated-preference approaches to deriving the VSL, our model is able to produce separate estimates of the marginal utilities of both avoided sick-years and avoided lost life-years. With these marginal utilities, we may infer willingness to pay to avoid a wide range of adverse health profiles over an individual’s future life. Such estimates are particularly important for ex ante benefit-cost analyses of environmental, health, or safety interventions where costs must be incurred now to reduce health risks that will not materialize fully until much later. The model generalizes the single-period, single-risk models (typically used with revealed-preference data to produce single-valued VSL estimates) in that we allow individuals to substitute across health risks with different time profiles. We evaluate our model using data from an extensive national survey that contains a set of randomized choice experiments. The model generalizes the single-period, single-risk models (typically used with revealed-preference data to produce single-valued VSL estimates) in that we allow individuals to substitute across health risks with different time profiles. We evaluate our model using data from an extensive national survey that contains a set of randomized choice experiments.
We develop and test an empirical model of individuals’ intertemporal demands for health risk-mitigation programs over the remaining years of their lives. We estimate this model using data from an innovative national survey of demand for preventative health care. We find qualified support for the Erhlich life-cycle model, which predicts that individuals expect to derive increasing marginal utility from reducing health risks that come to bear later in their lives. We also find, however, that as individuals age, there appears to be a systematic downward shift in this schedule of marginal utility for risk reduction at future ages. Our model improves on earlier work by differentiating between the respondent’s current age and the future ages at which they would experience adverse health states. Using age-specific demand schedules, we simulate age-specific values for avoided future statistical health states for risk mitigation policies with various latency periods. Our empirical results contribute to the debate about whether a senior death discount should be employed in public policymaking with respect to health risks.
We evaluate the effects of actual and expected morbidity on individuals’ demands for both life-saving policies and preventative health care. Empirically, we then test hypotheses about how actual and expected morbidity affect individuals’ health-seeking and risk-mitigating behavior. Having had an earlier illness increases individuals’ demands for programs for the prevention of a recurrence by at least threefold. Individuals’ demands for the targeted illness programs decrease when their subjective risk assessments of other illnesses are high relative to the targeted illness. Finally, we find that actual morbidity does cause a significant increase in individuals’ risk aversion when facing health risks. The morbidity effect appears to act by increasing the marginal utility of income overall but also by making this marginal utility decline more quickly with increases in income.
Correcting for as many sources of sample selection bias is essential to ensure that our estimates of demand for risk reductions truly are representative of the U.S. population. To date, we know of no study that represents the general adult and senior population as we plan to do. The Office of Management and Budget recently has begun to focus extensively on methodological issues such as nonresponse biases in survey data. To address and preempt such concerns we seek to expand our focus on data quality issues.
Researchers frequently acknowledge several reasons for possible nonrepresentativeness in surveys of samples drawn from large consumer panels. We model the selection process for one such panel, starting with a random-digit-dialed set of initial contacts and following these cases through a number of distinct attrition opportunities, ending with one sample drawn for an actual survey and the individuals who chose to respond to it. Using GIS methods, we match over 525,000 random-digit dialing addresses or telephone exchanges to the corresponding county and the most appropriate Census tract. We use a set of 15 orthogonal factors based on Census tract characteristics, plus county voting percentages for candidates Gore and Nader in the 2000 presidential election. We find many statistically significant determinants of attrition at our different attrition opportunities. To illustrate the effects of selection, we consider a second subsample where survey respondents expressed their opinions about the proper role of government in terms of environmental, health, and safety regulations. In a formal maximum likelihood selection model, we find some evidence of a slight liberal (proregulation) bias that may stem from nonrandom selection, but the effect is not statistically significant and the hypothesis of “no liberal/conservative bias” cannot be summarily rejected. Less sophisticated models in the class of propensity score corrections show minimal significant effects on selection propensity in the regulatory preference outcome models, but the distortions are quantitatively tiny.
Future Activities:
We will complete the three remaining objectives. Specifically, we will: (1) develop a structural model of demand for public prevention programs that reduce risk in a variety of risk exposure contexts and a variety of health risk outcomes; (2) develop a similar model of demand for treatment programs that reduce individuals’ risk of remaining ill or dying once they have become ill; and (3) compare demand for ex ante prevention programs versus ex post treatment programs that reduce the risk of various states of illness and premature death.
Journal Articles:
No journal articles submitted with this report: View all 15 publications for this projectSupplemental Keywords:
air, ambient air, water, drinking water, exposure, risk, risk assessment, health effects, human health, sensitive populations, dose-response, carcinogen, population, children, elderly, stressor, age, race, sex, ethnic groups, susceptibility, life-cycle analysis, decision making, cost benefit, conjoint analysis, nonmarket valuation, contingent valuation, survey, psychological, preferences, public good, Bayesian, socioeconomic, willingness-to-pay, compensation, analytical, surveys, measurement methods,, RFA, Economic, Social, & Behavioral Science Research Program, Health, Scientific Discipline, Health Risk Assessment, Risk Assessments, Susceptibility/Sensitive Population/Genetic Susceptibility, decision-making, Environmental Statistics, genetic susceptability, Sociology, Social Science, Economics & Decision Making, mortality rates, decision making, valuation of mortality, value of statistical life (VSL), environmental policy, health valuation models, mortality studies, models, representativeness, mortality, mortality risks, conjoint analysisProgress and Final Reports:
Original AbstractThe perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Conclusions drawn by the principal investigators have not been reviewed by the Agency.